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- All Subjects: Education
- All Subjects: deep learning
- Creators: Computer Science and Engineering Program
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Description
As a member of the National Academy of Engineering’s Grand Challenge Scholars Program (GCSP) and the new Next Generation Service Corps (NGSC), I began this project interested in investigating the benefits and outcomes of these programs on my development throughout my undergraduate experience. Since interdisciplinarity is a core component of both programs, my thesis focused on the development and analysis of a survey to measure the interdisciplinary competence of undergraduate students in various programs and majors throughout ASU. In order to develop the survey items, we adapted questions by Lattuca, et al, which only analyzed the interdisciplinary competence of engineering students. Based on our responses, the quantitative data surfaced some interesting discrepancies between students in engineering and non-engineering majors. Broadly, the data also showed that students in GCSP and NGSC have higher interdisciplinary competence, implying there may be some benefits to both. Additionally, a preliminary theme analysis of the qualitative data seems to demonstrate that students appreciate a wide variety of opportunities to be exposed to disciplines outside of their primary major, and programs such as GCSP and NGSC which highlight interdisciplinarity expose students to opportunities they otherwise wouldn’t have known about. In the future, I would recommend evaluating the impact of students’ motivations for joining each program and examining the possible implications on their interdisciplinary competence. There are other outcomes that weren’t examined as part of this study, so it may also be interesting for future researchers to investigate other components of each program like the impacts of service learning or entrepreneurial experiences.
ContributorsChen, Diana Karen (Author) / Ganesh, Tirupalavanam (Thesis director) / Trowbridge, Amy (Committee member) / Watts College of Public Service & Community Solut (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Deep learning and AI have grabbed tremendous attention in the last decade. The substantial accuracy improvement by neural networks in common tasks such as image classification and speech recognition has made deep learning as a replacement for many conventional machine learning techniques. Training Deep Neural networks require a lot of data, and therefore vast of amounts of computing resources to process the data and train the model for the neural network. The most obvious solution to solving this problem is to speed up the time it takes to train Deep Neural networks.
AI and deep learning workloads are different from the conventional cloud and mobile workloads, with respect to: (1) Computational Intensity, (2) I/O characteristics, and (3) communication pattern. While there is a considerable amount of research activity on the theoretical aspects of AI and Deep Learning algorithms that run with greater efficiency, there are only a few studies on the infrastructural impact of Deep Learning workloads on computing and storage resources in distributed systems.
It is typical to utilize a heterogeneous mixture of CPU and GPU devices to perform training on a neural network. Google Brain has a developed a reinforcement model that can place training operations across a heterogeneous cluster. Though it has only been tested with local devices in a single cluster. This study will explore the method’s capabilities and attempt to apply this method on a cluster with nodes across a network.
AI and deep learning workloads are different from the conventional cloud and mobile workloads, with respect to: (1) Computational Intensity, (2) I/O characteristics, and (3) communication pattern. While there is a considerable amount of research activity on the theoretical aspects of AI and Deep Learning algorithms that run with greater efficiency, there are only a few studies on the infrastructural impact of Deep Learning workloads on computing and storage resources in distributed systems.
It is typical to utilize a heterogeneous mixture of CPU and GPU devices to perform training on a neural network. Google Brain has a developed a reinforcement model that can place training operations across a heterogeneous cluster. Though it has only been tested with local devices in a single cluster. This study will explore the method’s capabilities and attempt to apply this method on a cluster with nodes across a network.
ContributorsNguyen, Andrew Hoang (Author) / Zhao, Ming (Thesis director) / Biookaghazadeh, Saman (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
In modern society, computer science (CS) professionals are necessary in the workforce. A growing number of fields and disciplines require the analytical and programming skills that come from a CS education. Despite the growing demand for programmers, the dropout rate within undergraduate CS programs remains high. In an effort to improve retention and make CS more accessible, I prototyped a mobile application that will help students through the principal deterrents that students face in their undergraduate years. Utilizing survey responses from 51 peers I determined the core courses and concepts within the CS curriculum that provoked the most concern to select the topics covered in the mobile application. The results show that the major barrier courses are CSE 310: Data Structures and Algorithms, CSE 340: Principles of Programming Languages, and CSE 355: Introduction to Theoretical Computer Science. Also using interviews and market research, I went through an iterative design process until I arrived at my final prototype that provides users a visual timeline of their program, examples for each individual topic, the ability to interact with other users, and create quizzes covering content they learned. This prototype is intended to lead to a fully developed application that will prepare and encourage students to further their professional careers in CS.
ContributorsRoldan, Jorge (Author) / Ganesh, Tirupalavanam (Thesis director) / Trowbridge, Amy (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
In response to a national call within STEM to increase diversity within the sciences, there has been a growth in science education research aimed at increasing participation of underrepresented groups in science, such as women and ethnic/racial minorities. However, an underexplored underrepresented group in science are religious students. Though 82% of the United States population is religiously affiliated, only 52% of scientists are religious (Pew, 2009). Even further, only 32% of biologists are religious, with 25% identifying as Christian (Pew, 2009; Ecklund, 2007). One reason as to why Christian individuals are underrepresented in biology is because faculty may express biases that affect students' ability to persist in the field of biology. In this study, we explored how revealing a Christian student's religious identity on science graduate application would impact faculty's perception of the student during the biology graduate application process. We found that faculty were significantly more likely to perceive the student who revealed their religious identity to be less competent, hirable, likeable, and faculty would be less likely to mentor the student. Our study informs upon possible reasons as to why there is an underrepresentation of Christians in science. This further suggests that bias against Christians must be addressed in order to avoid real-world, negative treatment of Christians in science.
ContributorsTruong, Jasmine Maylee (Author) / Brownell, Sara (Thesis director) / Gaughan, Monica (Committee member) / Barnes, Liz (Committee member) / School of Life Sciences (Contributor) / W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Hackathons are 24-36 hour events where participants are encouraged to learn, collaborate, and build technological inventions with leaders, companies, and peers in the tech community. Hackathons have been sweeping the nation in the recent years especially at the collegiate level; however, there is no substantial research or documentation of the actual effects of hackathons especially at the collegiate level. This makes justifying the usage of valuable time and resources to host hackathons difficult for tech companies and academic institutions. This thesis specifically examines the effects of collegiate hackathons through running a collegiate hackathon known as Desert Hacks at Arizona State University (ASU). The participants of Desert Hacks were surveyed at the start and at the end of the event to analyze the effects. The results of the survey implicate that participants have grown in base computer programming skills, inclusion in the tech community, overall confidence, and motivation for the technological field. Through these results, this study can be used to help justify the necessity of collegiate hackathons and events similar.
ContributorsLe, Peter Thuan (Author) / Atkinson, Robert (Thesis director) / Chavez-Echeagaray, Maria Elena (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
Learning student names has been promoted as an inclusive classroom practice, but it is unknown whether students value having their names known by an instructor. We explored this question in the context of a high-enrollment active-learning undergraduate biology course. Using surveys and semistructured interviews, we investigated whether students perceived that instructors know their names, the importance of instructors knowing their names, and how instructors learned their names. We found that, while only 20% of students perceived their names were known in previous high-enrollment biology classes, 78% of students perceived that an instructor of this course knew their names. However, instructors only knew 53% of names, indicating that instructors do not have to know student names in order for students to perceive that their names are known. Using grounded theory, we identified nine reasons why students feel that having their names known is important. When we asked students how they perceived instructors learned their names, the most common response was instructor use of name tents during in-class discussion. These findings suggest that students can benefit from perceiving that instructors know their names and name tents could be a relatively easy way for students to think that instructors know their names. Academic self-concept is one's perception of his or her ability in an academic domain compared to other students. As college biology classrooms transition from lecturing to active learning, students interact more with each other and are likely comparing themselves more to students in the class. Student characteristics, such as gender and race/ethnicity, can impact the level of academic self-concept, however this has been unexplored in the context of undergraduate biology. In this study, we explored whether student characteristics can affect academic self-concept in the context of a college physiology course. Using a survey, students self-reported how smart they perceived themselves in the context of physiology compared to the whole class and compared to the student they worked most closely with in class. Using logistic regression, we found that males and native English speakers had significantly higher academic self-concept compared to the whole class compared with females and non-native English speakers, respectively. We also found that males and non-transfer students had significantly higher academic self-concept compared to the student they worked most closely with in class compared with females and transfer students, respectively. Using grounded theory, we identified ten distinct factors that influenced how students determined whether they are more or less smart than their groupmate. Finally, we found that students were more likely to report participating less than their groupmate if they had a lower academic self-concept. These findings suggest that student characteristics can influence students' academic self-concept, which in turn may influence their participation in small group discussion.
ContributorsKrieg, Anna Florence (Author) / Brownell, Sara (Thesis director) / Stout, Valerie (Committee member) / Cooper, Katelyn (Committee member) / School of Life Sciences (Contributor) / School of Politics and Global Studies (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Programming is quickly becoming as ubiquitous a tool as general mathematics. The technology field is progressing at an exponential rate and driving this constantly evolving field forward requires competent software developers. Elementary and high school educational facilities do not currently express the importance of the computer science field. Computer science is not a required course in high school and nearly impossible to find at a middle school level. This lack of exposure to the field at a young age handicaps aspiring developers by not providing them with a foundation to build on when seeking a degree. This paper revolves around the development of a virtual world that encompasses principles of programming in a video game structure. The use of a virtual world-based game was chosen under the hypothesis that embedding programming instruction into a game through problem-based learning is more likely to engage young students than more traditional forms of instruction. Unlike the traditional method of instruction, a virtual world allows us to "deceive" the player into learning concepts by implicitly educating them through fun gameplay mechanics. In order to make our video game robust and self-sufficient, we have developed a predictive recursive descent parser that will validate any user-generated solutions to pre-defined logical platforming puzzles. Programming topics taught with these problems range from binary numbers to while and for loops.
ContributorsWest, Grant (Co-author) / Kury, Nizar (Co-author) / Nelson, Brian (Thesis director) / Kobayashi, Yoshihiro (Committee member) / Computer Science and Engineering Program (Contributor) / Computing and Informatics Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Learning to program is no easy task, and many students experience their first programming during their university education. Unfortunately, programming classes have a large number of students enrolled, so it is nearly impossible for professors to associate with the students at an individual level and provide the personal attention each student needs. This project aims to provide professors with a tool to quickly respond to the current understanding of the students. This web-based application gives professors the control to quickly ask Java programming questions, and the ability to see the aggregate data on how many of the students have successfully completed the assigned questions. With this system, the students are provided with extra programming practice in a controlled environment, and if there is an error in their program, the system will provide feedback describing what the error means and what steps the student can take to fix it.
ContributorsVillela, Daniel Linus (Author) / Kobayashi, Yoshihiro (Thesis director) / Nelson, Brian (Committee member) / Hsiao, Sharon (Committee member) / Computing and Informatics Program (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Bioscience High School, a small magnet high school located in Downtown Phoenix and a STEAM (Science, Technology, Engineering, Arts, Math) focused school, has been pushing to establish a computer science curriculum for all of their students from freshman to senior year. The school's Mision (Mission and Vision) is to: "..provide a rigorous, collaborative, and relevant academic program emphasizing an innovative, problem-based curriculum that develops literacy in the sciences, mathematics, and the arts, thus cultivating critical thinkers, creative problem-solvers, and compassionate citizens, who are able to thrive in our increasingly complex and technological communities." Computational thinking is an important part in developing a future problem solver Bioscience High School is looking to produce. Bioscience High School is unique in the fact that every student has a computer available for him or her to use. Therefore, it makes complete sense for the school to add computer science to their curriculum because one of the school's goals is to be able to utilize their resources to their full potential. However, the school's attempt at computer science integration falls short due to the lack of expertise amongst the math and science teachers. The lack of training and support has postponed the development of the program and they are desperately in need of someone with expertise in the field to help reboot the program. As a result, I've decided to create a course that is focused on teaching students the concepts of computational thinking and its application through Scratch and Arduino programming.
ContributorsLiu, Deming (Author) / Meuth, Ryan (Thesis director) / Nakamura, Mutsumi (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Medical records are increasingly being recorded in the form of electronic health records (EHRs), with a significant amount of patient data recorded as unstructured natural language text. Consequently, being able to extract and utilize clinical data present within these records is an important step in furthering clinical care. One important aspect within these records is the presence of prescription information. Existing techniques for extracting prescription information — which includes medication names, dosages, frequencies, reasons for taking, and mode of administration — from unstructured text have focused on the application of rule- and classifier-based methods. While state-of-the-art systems can be effective in extracting many types of information, they require significant effort to develop hand-crafted rules and conduct effective feature engineering. This paper presents the use of a bidirectional LSTM with CRF tagging model initialized with precomputed word embeddings for extracting prescription information from sentences without requiring significant feature engineering. The experimental results, run on the i2b2 2009 dataset, achieve an F1 macro measure of 0.8562, and scores above 0.9449 on four of the six categories, indicating significant potential for this model.
ContributorsRawal, Samarth Chetan (Author) / Baral, Chitta (Thesis director) / Anwar, Saadat (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05